Amazon Personalize is excited to announce the new Trending-Now recipe to help you recommend items gaining popularity at the fastest pace among your users. Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users. It enables you to improve customer engagement by powering personalized product and content recommendations in websites, applications, and targeted marketing campaigns. You can get started without any prior ML experience, using APIs to easily build sophisticated personalization capabilities in a few clicks. All your data is encrypted to be private and secure, and is only used to create recommendations for your users.

Today's customer expects the ability to engage with businesses through various communication channels like email, SMS, Push notifications, and in-app notifications when they have a question or need a problem resolved. SMS is one of the fastest growing communication channels, and we've seen that customers enjoy the ease and speed of texting for help versus traditional call channels. However, building an SMS system at scale to address millions of inquiries can be challenging for even the most advanced IT departments. Research also shows that customers prefer a personalized experience over a generic one, but using agents or employees to personalize millions of messages on a case-by-case basis is not practical. To solve this problem, we can use Amazon Pinpoint, AWS' multichannel communication service, to interact in personalized 2-way SMS messages with customers.

We're excited to announce that Amazon Personalize now lets you measure how your personalized recommendations can help you achieve your business goals. After specifying the metrics that you want to track, you can identify which campaigns and recommenders are most impactful and understand the impact of recommendations on your business metrics. All customers want to track the metric that is most important for their business. For example, an online shopping application may want to track two metrics: the click-through rate (CTR) for recommendations and the total number of purchases. A video-on-demand platform that has carousels with different recommenders providing recommendations may wish to compare the CTR or watch duration.

This is a guest post by Ramzi Alqrainy, Chief Technology Officer, The Chefz. The Chefz is a Saudi-based online food delivery startup, founded in 2016. At the core of The Chefz's business model is enabling its customers to order food and sweets from top elite restaurants, bakeries, and chocolate shops. In this post, we explain how The Chefz uses Amazon Personalize filters to apply business rules on recommendations to end-users, increasing revenue by 35%. Food delivery is a growing industry but at the same time is extremely competitive.

Today, we're excited to announce a new similar items recommendation recipe (aws-similar-items) in Amazon Personalize that helps you leverage your users' interaction histories and what you know about the items in your catalog to deliver relevant recommendations. Across Amazon, we provide personalized experiences for each of our users, and based on a user's interests, we change their experiences and the items they see. Visitors are often recommended items that users with similar histories have interacted with. These recommendations are called similar items, and they help users discover items relevant to what they're watching or purchasing. By taking into account the item a user is engaged with, we can improve engagement and conversion.

Data can be used in a variety of ways to satisfy the needs of different business units, such as marketing, sales, or product. In this post, we focus on using data to create personalized recommendations to improve end-user engagement. Most ecommerce applications consume a huge amount of customer data that can be used to provide personalized recommendations; however, that data may not be cleaned or in the right format to provide those valuable insights. The goal of this post is to demonstrate how to use AWS Glue to extract, transform, and load your JSON data into a cleaned CSV format. We then show you how to run a recommendation engine powered by Amazon Personalize on your user interaction data to provide a tailored experience for your customers.

We're excited to announce new efficiency improvements for Amazon Personalize. These improvements decrease the time required to train solutions (the machine learning models trained with your data) by up to 40% and reduce the latency for generating real-time recommendations by up to 30%. Amazon Personalize enables you to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations--no ML expertise required. Amazon Personalize provisions the necessary infrastructure and manages the entire ML pipeline, including processing the data, identifying features, using the best algorithms, and training, optimizing, and hosting the models. When serving recommendations, minimizing the time your system takes to generate and serve a recommendation improves conversion.

We're pleased to announce enhancements to recommendation filters in Amazon Personalize, which provide you greater control on recommendations your users receive by allowing you to exclude or include items to recommend based on criteria that you define. For example, when recommending products for your e-retail store, you can exclude unavailable items from recommendations. If you're recommending videos to users, you can choose to only recommend premium content if the user is in a particular subscription tier. You typically address this by writing custom code to implement their business rules, but you can now save time and streamline your architectures by using recommendation filters in Amazon Personalize. Based on over 20 years of personalization experience, Amazon Personalize enables you to improve customer engagement by powering personalized product and content recommendations and targeted marketing promotions.

This is a guest post by Mark Wood at Pulselive. In their own words, "Pulselive, based out of the UK, is the proud digital partner to some of the biggest names in sports." At Pulselive, we create experiences sports fans can't live without; whether that's the official Cricket World Cup website or the English Premier League's iOS and Android apps. One of the key things our customers measure us on is fan engagement with digital content such as videos. But until recently, the videos each fan saw were based on a most recently published list, which wasn't personalized.

With rapid advancements in machine learning (ML) techniques over the past decade, intelligent decision-making and prediction systems are poised to transform productivity and lead to significant economic gains. A study conducted by PwC Global concludes that by the end of this decade, the total positive impact of artificial intelligence (AI) on the global economy could be above $15 trillion, driven mostly by enhancements in consumer products. To make that happen, however, businesses must make strategic investments in the type of technology that moves AI projects into production (productionizing) and helps customers deploy them. Unfortunately, PwC's survey reveals the percentage of executives planning to deploy AI has gone down from 20 percent a year ago to only 4 percent at the beginning of 2020. The primary reason for this decrease is the gap between the growing volume of data and data-driven modeling capabilities, and the necessary skills and toolsets.